Almost all Electronic Health Record Systems (“EHR-S”), store information in Relational Database Management Systems (“RDBMS”). RDBMS are an improvement over the previously used flat file database technology which, based on a relatively rigid table-like structure of columns and rows, was inflexible, did not lend itself to ongoing maintenance modifications, such as adding or removing columns, and which became dramatically slower as the volume of data stored in those databases grew.
RDBMSs, although unquestionably an improvement over flat file technology, also suffer from various drawbacks. RDBMSs are based on storing data in numerous separate tables, which are linked together via mechanisms such as Primary Keys and Foreign Keys into what are collectively called databases. A single patient's EHR data may be spread over hundreds of different tables, and can only be presented to the user, after significant computer processing to follow the links from one table to the next and to then combine the collected data into either human- or machine-intelligible information. This limitation effectively means that the end user is dependent on the software manufacturer that designed the database. For all practical purposes, the end user is not able to view or use the data in the EHR-S database—even though that data belongs to the end user—without doing so in a manner approved by the software manufacturer. Moreover, the end user is not able to modify the structure and operation of the EHR-S without the permission and support of the software manufacturer. This situation puts significant control in the hands of the software manufacturer, resulting in substantially higher costs to the end user and a substantial brake on innovation.
Another problem in the related art is the difficulty the Health Information Technology (“HIT”) industry is having in developing effective ways of sharing health data. It is widely recognized that the power of electronic health records cannot be fully utilized until these records may be easily and quickly shared as the need arises, and yet do so in a manner that preserves the confidentiality of patient health information. There is little disagreement that solving this problem will lead to major benefits including the following:                The close collaboration of multiple providers in determining and delivering care to the individual patient, in contrast to the prior art, in which each provider has substantially difficulty obtaining, in a timely manner, detailed and accurate information about the care being provided by other healthcare providers;        The resilience of the health care system to natural and human disasters that destroy all health data within a geographic area, by having duplicates of the data ready to replace the destroyed data at a moment's notice; and        The development and improvement of healthcare procedures and regimen, by allowing sustained research on substantially larger and richer aggregations of health data than are now practical.        
These benefits, though powerful, have proved elusive because of the architecture of currently available EHR-S. Presently, health data in the United States is stored in hundreds of thousands of separate databases in tens of thousands of different locations. Creating a National Health Information Network (“NHIN”) to seamlessly and effectively share data across all of those potential points of failure has proved to be a daunting task.
There have been two major approaches in the related art to solving this problem: the Federated Model and the Centralized Model. In the centralized model, a copy of the health data is stored centrally in community or regional databases. In the federated model, the healthcare providers in a given area register with the regional Health Information Exchange (“HIE”) but don't actually share data until that data is requested by a specific user.
Both methods have exhibited substantial problems in meeting the objectives of the NHIN. The centralized model is clearly better suited towards meeting those objectives. Indeed, the federated model does not adequately meet any of the above objectives. The objectives of resiliency and large volume research are not really possible as long as the data is locked in thousands of different databases controlled by thousands of separate governance authorities. The objective of enabling care collaboration is partially met, in that the provider can, usually, request and receive information on the patient's care from other providers. However, due to the federated nature of this approach, this information is not available in a timely manner, nor is the information flow reliable, due to the many points of potential failure.
Despite those difficulties with the federated model, the centralized model has an even more serious problem: few stakeholders have been willing to absorb the expense of maintaining the considerable amount of patient data at centralized locations. Those places where the centralized model has been attempted have not been successful, except to the extent that government and philanthropic bodies have agreed to fund the effort.
As a result, most viable efforts at establishing HIEs are using the federated model. The federated model is cheaper, but there have still been problems building a business case among the stakeholders for supporting the ongoing expense of the HIE. Nevertheless, federated HIEs are being successfully deployed. The usefulness of these federated HIEs, however, remains to be proven.
As mentioned above, federated HIEs have issues with data availability. Since the data is stored at numerous sites not controlled by the HIE, the requesting organization doesn't really know what health data for the patient is available until it is requested. Moreover, the particular information may be stored at a facility that is currently offline. As a result of these difficulties, the federated model of HIE is not very reliable for the task of care coordination unless the parties to the exchange have an ongoing business relationship before the exchange or the originator of the data exchange is the sender and not the receiver. In other words, federated HIEs are successful at sharing data between business partners and at sending data from one provider to another. They have not, however, been very successful at informing care providers about the activities of non-business partners. Thus, federated HIEs, while a viable enhancement of previously existing interface technology, have had limited success at improving care collaboration.
Federated HIEs have been shown to be successful at providing a mechanism for providers to submit required data to regulatory authorities and voluntary quality improvement organizations. However, while this ability is clearly beneficial, it is not really an enhancement over what is available with point to point transmission between business partners, such as with web service exchanges. It simply provides a more expensive way of achieving the same result (because the HIE in the middle must be paid for).
Beyond these multiple problems with federated HIEs there is a significant data validity risk with all current Health Information Exchanges, whether they use the federated model or the centralized model. Currently health information is stored in hundreds of thousands of proprietary databases designed and maintained by one of the hundreds of vendors offering EHR-S system. Before information can be exchanged, it must be extracted from these systems and translated to a common format understood by both the sending and receiving systems. At the receiving end, the information must be translated again into a format compatible with the receiving system's design. As a result, the data is displayed and used at the receiving end in a different format and context than it was originally created in. This runs the risk of potentially serious translation errors.
Due to all of the various issues listed above, and other unlisted issues, the goal of large-scale data sharing for care collaboration, health data resiliency and large-scale research has so far been unmet.
We have discussed several major problems with the prior art currently used in health information technology. The two most problematic of these issues has been the high cost and inflexibility of EHR-S and other HIT systems, and the inability to achieve an effective health information network despite many efforts and high costs. Both of these problems are consequences of the legacy architecture of storing health data in many separate databases created by hundreds of vendors with widely different design objectives.